7 Computer Vision Projects for All Levels

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Computer vision is a fascinating field that combines machine learning and image processing to enable machines to interpret and make decisions based on visual data. Whether you are a beginner or an advanced practitioner, there are numerous projects you can undertake to build a strong portfolio and learn about new techniques, frameworks, and types of computer vision problems.

In this blog, we will review 7 computer vision projects for beginners, intermediate, and advanced levels. Each project comes with detailed explanations, source code or guides, and datasets for you to start building your own project.

Beginner Computer Vision Projects

Beginner projects are ideal for newcomers to computer vision, focusing on fundamental tasks like image classification and face detection to build foundational skills.

1. Plant Disease Detection

 
Plant disease detection is an important application of computer vision in agriculture. You will learn to load, process, and augment the dataset, build your deep neural network model, and train the model on the dataset. This project helps in understanding image classification and contributes to sustainable agriculture by enabling early disease detection.

• Code Source: PlantVillage_classification (kaggle.com)
• Dataset: PlantVillage Dataset (kaggle.com)

2. Optical Character Recognition (English)

 
Optical character recognition technology allows computers to convert different types of documents, such as scanned paper documents, PDFs, or images captured by a digital camera, into editable and searchable data. 

In this project, you will use the English Handwritten Characters dataset to fine-tune a pre-trained model, enhancing its ability to recognize and digitize handwritten text. The goal is to improve the accuracy of recognition, which is a crucial skill for automating data entry processes.
 

• Code Source: Optical Character Recognition (OCR) using DL (kaggle.com)
• Dataset: English Handwritten Characters (kaggle.com)

3. American Sign Language Image Classification

 
This project involves building a model to classify images of American Sign Language (ASL) gestures. By using the American Sign Language Dataset, you can create a system that translates ASL into text, thereby aiding communication for the hearing impaired. This project is an excellent way to learn about multi-class classification using convolutional neural networks (CNNs). You will learn about image data analysis, model evaluation, and ways to improve your model.
 

• Source Code: Hand-Sign✊🖖: Multi-class classification CNN (97%) (kaggle.com)
• Dataset: American Sign Language Dataset (kaggle.com)

Intermediate Computer Vision Projects

 
Intermediate projects challenge learners with real-time processing and more sophisticated algorithms. Examples include object tracking and image captioning, which is a multimodal problem that requires knowledge of both Natural Language Processing (NLP) and computer vision.

4. Car Number Plate Recognition

 
Automatic Number Plate Recognition (ANPR) systems are widely used in traffic monitoring, parking management, and toll collection. The process involves collecting and labeling images for license plate detection, preprocessing the data, building and training a deep learning model for object detection, using the trained model to extract license plate regions for text recognition with an OCR model, and finally, creating an app that will extract the car number plate from the video in real-time.

• Source Code: Automatic Number Plate Recognition (kaggle.com)
• Dataset: Vehicle Number Plate Detection (kaggle.com)

5. Flickr Image Captioning

 
Image captioning involves generating textual descriptions for images. Using the Flickr Image dataset, you can build and train a Transformer model that describes the content of an image in natural language. This project combines computer vision and natural language processing, making it an exciting challenge for those looking to explore the intersection of these fields. 

• Source Code: Image Captioning | Transformers | Flickr30k (kaggle.com)
• Dataset: Flickr Image dataset (kaggle.com)

Advanced Computer Vision Projects

Advanced projects require a comprehensive grasp of computer vision concepts and programming skills, tackling complex tasks like autonomous vehicle navigation and medical image analysis.

6. Multi-person Pose Estimation and Tracking in Videos

 
Multi-frame human pose estimation in videos is difficult because of challenges such as motion blur and pose occlusions. These issues are difficult for static image models and traditional recurrent neural networks to handle. In this project, you will work with datasets like PoseTrack to track multiple people in videos. You will predict the location of key points such as hands and elbows, and also address the challenges of processing and understanding video data. 

• Source Code: Pose-Group/DCPose
• Dataset: PoseTrack Dataset

7. Anomaly Detection

 
Anomaly detection in images is crucial for identifying unusual patterns that do not conform to expected behavior. Using the MVTec AD dataset, you can develop models to detect defects in manufacturing processes or unusual activities in surveillance footage. This project is particularly relevant in quality control and security applications.

• Source Code: MVTec-AD : Anomaly Detection with Anomalib Library (kaggle.com)
• Dataset: MVTec AD (kaggle.com)

Conclusion

 
These 7 projects provide a complete journey through the world of computer vision. They cover everything from simple image classification to more complex tasks like pose estimation and anomaly detection. By working on these projects, you can gain a thorough understanding of computer vision techniques and how they are used in different industries. Whether you are a beginner or want to tackle more advanced challenges, these projects will help you build a strong portfolio and resume to get hired and advance in your career.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.